Module Details
Module Code: |
ZDAT C4100 |
Module Title:
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Advanced Data Analysis and Modelling
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Title:
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Advanced Data Analysis and Modelling
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Module Level:: |
8 |
Module Coordinator: |
Paula Rankin
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Module Author:: |
Rachael Carroll
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Module Description: |
To introduce the students to a wide variety of Data-Analysis, Statistical and Modelling Techniques. (The emphasis will be on description and usefulness of the techniques studied rather than with routine calculations). To analyse a number of practical problems using computer facilities. Understanding issues to consider when designing a trial. Understanding the key statistical components involved in the planning and conduct of clinical trials.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Describe the key elements for the importance of good experimental design and apply the appropriate data-analytic techniques. |
LO2 |
Describe and discuss key issues to consider when designing a clinical trial and the key statistical components involved in the planning and conduct of clinical trials. |
LO3 |
Apply and recognise practical situations where a statistical or a deterministic model is appropriate. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Data managment
Priciples of good data managment. Key elements of a good graph and data visualisation. Review of types of data, confidence intervals and P values. Understanding and interpreting treatment effects, statistical significance, effect size, The principle of parsimony.
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Statistical tests
Parametric vs nonparametric tests. Review of key statistical tests: Tests for differences in means. Student’s T-Test, Analysis-of-Variance (ANOVA). Correlations and Significance of regression. Linear, Polynomial and Multiple Regression.
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Experimental Design
Fundamental Principles of Good Design. Awareness of different types of outcomes and be able to select the appropriate statistical technique for the type of outcome and study design.
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Biostatistics
Bioassays, bioavailability and bioequivalence. Prevalence and incidence. Study designs: cross-sectional, cohort, case-control, experimental, randomised control trials. Efficacy, dose response relationship, placebos. Understanding different types of trial designs and be able to choose the relevant design for a given question.
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Clinical Trials
The role of the statistics in drug development. Understanding the key statistical components involved in the planning and conduct of clinical trials. Design configurations and issues, Parallel Group Design, Crossover Design, Factorial Designs. Design Techniques to Avoid Bias, Blinding, Randomization. Control groups, confounding factors. Statistical versus clinical significance. Study protocol.
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Deterministic models and pharmacokinetics
The application of differential equations including pharmacokinetics such as variation of drug and metabolic levels in various fluids and tissues of the body, compartment models for mixtures, rates of drug absorption and elimination, elimination half-life and dose determination for anesthetic drugs.
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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 30.00% |
Practical | 30.00% |
End of Module Formal Examination | 40.00% |
AssessmentsFull Time
End of Module Formal Examination |
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Reassessment Requirement |
Exam Board
It is at the discretion of the Examination Board as to what the qualifying criteria are.
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SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time |
Workload Type |
Workload Category |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
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Contact |
No Description |
12 Weeks per Stage |
2.00 |
24 |
Practicals |
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Contact |
No Description |
12 Weeks per Stage |
2.00 |
24 |
Independent Learning Time |
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Non Contact |
No Description |
15 Weeks per Stage |
5.13 |
77 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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Recommended Book Resources |
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Douglas C. Montgomery. (2020), Design and Analysis of Experiments, John Wiley & Sons, p.688, [ISBN: 1119722101].
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Karl E Peace. (2020), Statistical Issues in Drug Research and Development, CRC Press, p.384, [ISBN: 9780367580179].
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P.Millard A. Krause. (2012), Applied statistics in the pharmaceutical industry, 1. Springer, [ISBN: 0387988149].
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S. Senn. (2010), Statistical issues in drug development, 1. [ISBN: 9780470018774].
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D. G Zill. (2018), First course in differential equations with modelling applications, 11. Cengage Learning, [ISBN: 9781305965720].
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A. Pocock. (2015), Clinical Trials A practical approach, Wiley, [ISBN: 0471901555].
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A.J Cohen. (2008), Statistics and data with R, Wiley, [ISBN: 9780470758052].
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Arthur J. Atkinson. (2012), Principles of Clinical Pharmacology, Academic Press, p.626, [ISBN: 9780123854711].
| Supplementary Book Resources |
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C. Ralph Buncher,Jia-Yeong Tsay. (2005), Statistics In the Pharmaceutical Industry, 3rd Edition, CRC Press, p.504, [ISBN: 9780824754693].
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D. A. Berry. (1989), Statistical Methodology in the Pharmaceutical Sciences, CRC Press, p.592, [ISBN: 9780824781170].
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David J. Finney. (1987), Statistical Method in Biological Assay, Charles Griffin Book, p.522, [ISBN: 9780195205671].
| This module does not have any article/paper resources |
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This module does not have any other resources |
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